This paper proposes a hybrid radiance field representation for unbounded immersive light field reconstruction which supports high-quality rendering and aggressive view extrapolation. The key idea is to first formally separate the foreground and the background and then adaptively balance learning of them during the training process. To fulfill this goal, we represent the foreground and background as two separate radiance fields with two different spatial mapping strategies. We further propose an adaptive sampling strategy and a segmentation regularizer for more clear segmentation and robust convergence. Finally, we contribute a novel immersive light field dataset, named THUImmersive, with the potential to achieve much larger space 6DoF immersive rendering effects compared with existing datasets, by capturing multiple neighboring viewpoints for the same scene, to stimulate the research and AR/VR applications in the immersive light field domain. Extensive experiments demonstrate the strong performance of our method for unbounded immersive light field reconstruction.

本文提出了一种用于无界沉浸式光场重建的混合辐射场表示方法，该方法支持高质量渲染和激进的视点外推。核心思想是首先正式分离前景和背景，然后在训练过程中自适应地平衡它们的学习过程。为了实现这一目标，我们将前景和背景分别表示为两个具有不同空间映射策略的独立辐射场。我们进一步提出了一种自适应采样策略和一种分割正则化器，以获得更清晰的分割和稳健的收敛性。最后，我们贡献了一个新颖的沉浸式光场数据集，名为THUImmersive，通过捕获同一场景的多个相邻视点，该数据集有可能与现有数据集相比实现更大的空间6DoF沉浸式渲染效果，从而刺激沉浸式光场领域的研究和AR/VR应用。广泛的实验展示了我们方法在无界沉浸式光场重建方面的强大性能。